class TestDataset(Dataset): def __init__(self, folder_path, hr, lr): super(TestDataset, self).__init__() self.folder_path = folder_path self.hr = hr self.lr = lr self.hr_path = os.path.join(self.folder_path, self.hr) self.lr_path = os.path.join(self.folder_path, self.lr) self.hr_list = [entry.name for entry in os.scandir(self.hr_path) if entry.is_file()] self.lr_list = [entry.name for entry in os.scandir(self.lr_path) if entry.is_file()] self.filenames = self.hr_list self.transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def __getitem__(self, index): self.hr_name = self.hr_list[index] self.lr_name = self.lr_list[index] self.hr_item_path = os.path.join(self.hr_path, self.hr_name) self.lr_item_path = os.path.join(self.lr_path, self.lr_name) hr_img = Image.open(self.hr_item_path).convert("RGB") lr_img = Image.open(self.lr_item_path).convert("RGB") hr_img = self.transform(hr_img) lr_img = self.transform(lr_img) return lr_img, hr_img def __len__(self): return len(self.filenames)

这是一个 Python 代码段，用于定义一个名为 TestDataset 的数据集类。该类接受三个参数：文件夹路径、高分辨率图像文件夹名称和低分辨率图像文件夹名称。它通过扫描指定文件夹中的文件来获取高分辨率和低分辨率图像的文件名列表，并将其存储在 hr_list 和 lr_list 中。在 __getitem__ 方法中，它会打开指定文件夹中的图像文件，并将其转换为张量，并返回低分辨率和高分辨率图像的张量。在 __len__ 方法中，它返回文件名列表的长度。

相关问题

用pytorch写一个深度学习CNN网络，训练集是40个矩阵和它们的标签：40个14×14的numpy矩阵文件在一个名为1MATRICE的文件夹中，文件名为1.npy~50.npy；40个标签文件在一个名为1LABEL的文件夹中，文件名为label_0.npy~label_50.npy，其中前20个值为0，属于PD类别，后30个值为1，属于SWEDD类别。验证集为6个14×14的矩阵，用softmax输出两个类别的概率。

好的，以下是一个基本的CNN网络的代码示例，您需要根据您的数据集进行调整和修改。首先，您需要导入所需的库和模块，如下所示：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import numpy as np
import os

接下来，您需要定义一个自定义数据集类，如下所示：

class MyDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.matrices = []
        self.labels = []

        # Load matrices and labels
        for i in range(1, 41):
            matrix_path = os.path.join(self.root_dir, str(i) + 'MATRICE', str(i) + '.npy')
            matrix = np.load(matrix_path)
            self.matrices.append(matrix)

            label_path = os.path.join(self.root_dir, str(i) + 'LABEL', 'label_' + str(i-1) + '.npy')
            label = np.load(label_path)
            self.labels.append(label)

    def __len__(self):
        return len(self.matrices)

    def __getitem__(self, idx):
        matrix = self.matrices[idx]
        label = self.labels[idx]

        if self.transform:
            matrix = self.transform(matrix)

        return matrix, label

然后，您需要定义一个CNN模型，如下所示：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.batchnorm1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.batchnorm2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(32 * 3 * 3, 64)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(64, 2)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.batchnorm1(x)
        x = self.relu1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.batchnorm2(x)
        x = self.relu2(x)
        x = self.maxpool2(x)
        x = x.view(-1, 32 * 3 * 3)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        x = self.softmax(x)
        return x

接下来，您需要定义训练和测试函数，如下所示：

def train(model, train_loader, criterion, optimizer):
    model.train()
    train_loss = 0.0
    train_acc = 0.0

    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs.unsqueeze(1).float())
        loss = criterion(outputs, labels.long())
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_acc.double() / len(train_loader.dataset)

    return train_loss, train_acc


def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0.0
    test_acc = 0.0

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(test_loader):
            outputs = model(inputs.unsqueeze(1).float())
            loss = criterion(outputs, labels.long())

            test_loss += loss.item() * inputs.size(0)
            _, preds = torch.max(outputs, 1)
            test_acc += torch.sum(preds == labels.data)

        test_loss = test_loss / len(test_loader.dataset)
        test_acc = test_acc.double() / len(test_loader.dataset)

    return test_loss, test_acc

最后，您需要实例化并运行模型，如下所示：

# Instantiate the model
model = CNN()

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Instantiate the data loaders
train_dataset = MyDataset('/path/to/train/folder')
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
test_dataset = MyDataset('/path/to/test/folder')
test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)

# Train the model
for epoch in range(10):
    train_loss, train_acc = train(model, train_loader, criterion, optimizer)
    test_loss, test_acc = test(model, test_loader, criterion)
    print('Epoch: {} Train Loss: {:.4f} Train Acc: {:.4f} Test Loss: {:.4f} Test Acc: {:.4f}'.format(
        epoch, train_loss, train_acc, test_loss, test_acc))

请注意，这只是一个基本的CNN模型，您需要根据您的数据集进行调整和修改。另外，由于您的数据集非常小，您可能需要采取一些正则化措施来防止过度拟合。

Densenet实现灰色图像到灰色图像的代码。映射前后的每个图像都用一个100*4096的二维数组表示(一个二维数组保存在一个TXT中，已经保存成二维数组形式了)，映射前后的txt分别保存在两个文件夹中，每个文件夹有240个txt，加入数据归一化，数据集划分，训练过程，最后能实现输入一个二维数组可以输出映射后的二维数组的效果

首先，需要将数据读入并进行归一化处理，可以使用以下代码：

import os
import numpy as np

def read_data(folder_path):
    data = []
    for filename in os.listdir(folder_path):
        file_path = os.path.join(folder_path, filename)
        arr = np.loadtxt(file_path)
        arr = arr / 255.0 # 归一化
        data.append(arr)
    return np.array(data)

input_folder = "input_folder"
output_folder = "output_folder"

input_data = read_data(input_folder)
output_data = read_data(output_folder)

接下来，可以使用PyTorch实现Densenet模型。以下是一个简单的Densenet实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import densenet121

class Densenet(nn.Module):
    def __init__(self):
        super(Densenet, self).__init__()
        self.densenet = densenet121(pretrained=True)
        self.linear = nn.Linear(1000, 4096)

    def forward(self, x):
        x = self.densenet(x)
        x = self.linear(x)
        return x

接下来，可以定义数据集和数据加载器，使用PyTorch的内置函数进行训练。以下是一个简单的训练过程：

from torch.utils.data import Dataset, DataLoader

class MyDataset(Dataset):
    def __init__(self, input_data, output_data):
        self.input_data = input_data
        self.output_data = output_data
        
    def __len__(self):
        return len(self.input_data)
    
    def __getitem__(self, idx):
        input_arr = self.input_data[idx]
        output_arr = self.output_data[idx]
        return input_arr, output_arr

train_ratio = 0.8
train_size = int(len(input_data) * train_ratio)
train_input = input_data[:train_size]
train_output = output_data[:train_size]
test_input = input_data[train_size:]
test_output = output_data[train_size:]

train_dataset = MyDataset(train_input, train_output)
test_dataset = MyDataset(test_input, test_output)

batch_size = 10
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Densenet().to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

num_epochs = 10
for epoch in range(num_epochs):
    train_loss = 0.0
    for batch_idx, (input_arr, output_arr) in enumerate(train_loader):
        input_arr = input_arr.to(device)
        output_arr = output_arr.to(device)
        optimizer.zero_grad()
        output = model(input_arr.unsqueeze(1).float())
        loss = criterion(output, output_arr.unsqueeze(1).float())
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    train_loss /= len(train_loader)
    
    test_loss = 0.0
    with torch.no_grad():
        for batch_idx, (input_arr, output_arr) in enumerate(test_loader):
            input_arr = input_arr.to(device)
            output_arr = output_arr.to(device)
            output = model(input_arr.unsqueeze(1).float())
            loss = criterion(output, output_arr.unsqueeze(1).float())
            test_loss += loss.item()
        test_loss /= len(test_loader)

    print("Epoch {} Train Loss {:.6f} Test Loss {:.6f}".format(epoch+1, train_loss, test_loss))

最后，可以实现一个函数，输入一个二维数组，输出映射后的二维数组：

def map_array(arr):
    arr = arr / 255.0 # 归一化
    arr = torch.tensor(arr).unsqueeze(0).unsqueeze(0).float().to(device)
    with torch.no_grad():
        output = model(arr)
    return output.squeeze(0).squeeze(0).cpu().numpy() * 255.0

这样，就可以使用以上代码实现灰度图像到灰度图像的Densenet映射了。